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Verus® Research s $1 Million contract monitors anomalies for Space Vehicles Directorate of the Air Force Research Laboratory – SatNews

Verus® Research s $1 Million contract monitors anomalies for Space Vehicles Directorate of the Air Force Research Laboratory – SatNews
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United statesJ mark delgrandeJacob lutzVerus researchHarris corporationNavigation technologyPhase ii small business technology transferUniversity of coloradoResearch laboratory space vehicles directorateVerus research satellite software for anomaly monitoringNew mexico basedSpace vehicles directorateAir force research laboratoryBusiness technology transferSatellite softwareAnomaly monitoring

Machine Learning in Process Monitoring and Control for Wire-Arc Additi by Yuxing Li, Haochen Mu et al

Wire-arc additive manufacturing (WAAM) is an arc-based directed energy deposition approach that uses an electrical arc as a source of fusion to melt the wire feedstock and deposit layer by layer. It’s applicable in fabricating large-scale components. At this stage, there are still some issues that need to be researched deeply, such as manufacturing accuracy control, process parameters optimization, path planning, and online monitoring. Machine learning is a new emerging artificial intelligence technology, which is more and more applied in modern industry. In this study, a machine learning based control algorithm was applied in melt pool width control. To monitor the WAAM process, deep learning algorithms were applied in anomalies recognition. At the same time, machine learning methods were employed to predict the deposited surface roughness during the WAAM process.

Additive manufacturingAnomaly monitoringDeep learningMachine learning

Vision-based melt pool monitoring for wire-arc additive manufacturing by Chunyang Xia, Zengxi Pan et al

Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, robot suspend, pores, cracking and so on. This study proposed to apply deep learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN (convolutional neural network) architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM.

Additive manufacturingAnomaly monitoringComputer visionDeep learning

Artificial Intelligence of Things Market Predictable to Booming Worldwide | Tachyus, SAS, IBM, HPE, SAP, Microsoft

Artificial Intelligence of Things Market Predictable to Booming Worldwide | Tachyus, SAS, IBM, HPE, SAP, Microsoft
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